Within the field of computing, many systems exist that can infer information about an individual based on various signals and use such information to provide the individual with enhanced services and personalized content. For example, an e-commerce Web site may recommend products of interest to an individual based on inferences drawn from the individual's previous purchases. As another example, a search engine may present advertisements of interest to an individual that are based on inferences drawn from the individual's previous searches. However, such systems lack transparency. Users have no way to understand what is known about them, or to confirm or deny information that has been inferred about them. Since these systems do not include their users in the process of collecting inferred data, they can make the users feel as if they are being spied upon. Moreover, such systems may present users with suggested content based on assumed, un-true or partially-true information.
Some systems have sought to address this issue by offering their users a way to proactively record certain user profile data or affinity for certain entities. For example, some social networking Web sites provide users with the opportunity to populate a social profile that may then be used to deliver personalized content. However, this kind of approach can be burdensome for the users, since they are required to manually input the user profile data or affinity information.